Microcollinearity between autopolyploid sugarcane and diploid sorghum genomes

Abstract Background Sugarcane (Saccharum spp.) has become an increasingly important crop for its leading role in biofuel production. The high sugar content species S. officinarum is an octoploid without known diploid or tetraploid progenitors. Commercial sugarcane cultivars are hybrids between S. officinarum and wild species S. spontaneum with ploidy at ~12×. The complex autopolyploid sugarcane genome has not been characterized at the DNA sequence level. Results The microsynteny between sugarcane and sorghum was assessed by comparing 454 pyrosequences of 20 sugarcane bacterial artificial chromosomes (BACs) with sorghum sequences. These 20 BACs were selected by hybridization of 1961 single copy sorghum overgo probes to the sugarcane BAC library with one sugarcane BAC corresponding to each of the 20 sorghum chromosome arms. The genic regions of the sugarcane BACs shared an average of 95.2% sequence identity with sorghum, and the sorghum genome was used as a template to order sequence contigs covering 78.2% of the 20 BAC sequences. About 53.1% of the sugarcane BAC sequences are aligned with sorghum sequence. The unaligned regions contain non-coding and repetitive sequences. Within the aligned sequences, 209 genes were annotated in sugarcane and 202 in sorghum. Seventeen genes appeared to be sugarcane-specific and all validated by sugarcane ESTs, while 12 appeared sorghum-specific but only one validated by sorghum ESTs. Twelve of the 17 sugarcane-specific genes have no match in the non-redundant protein database in GenBank, perhaps encoding proteins for sugarcane-specific processes. The sorghum orthologous regions appeared to have expanded relative to sugarcane, mostly by the increase of retrotransposons. Conclusions The sugarcane and sorghum genomes are mostly collinear in the genic regions, and the sorghum genome can be used as a template for assembling much of the genic DNA of the autopolyploid sugarcane genome. The comparable gene density between sugarcane BACs and corresponding sorghum sequences defied the notion that polyploidy species might have faster pace of gene loss due to the redundancy of multiple alleles at each locus.

Spatial and Temporal Variation of Archaeal, Bacterial and Fungal Communities in Agricultural Soils

Background: Soil microbial communities are in constant change at many different temporal and spatial scales. However, the importance of these changes to the turnover of the soil microbial communities has been rarely studied simultaneously in space and time. Methodology/Principal Findings: In this study, we explored the temporal and spatial responses of soil bacterial, archaeal and fungal beta-diversities to abiotic parameters. Taking into account data from a 3-year sampling period, we analyzed the abundances and community structures of Archaea, Bacteria and Fungi along with key soil chemical parameters. We questioned how these abiotic variables influence the turnover of bacterial, archaeal and fungal communities and how they impact the long-term patterns of changes of the aforementioned soil communities. Interestingly, we found that the bacterial and fungal b-diversities are quite stable over time, whereas archaeal diversity showed significantly higher fluctuations. These fluctuations were reflected in temporal turnover caused by soil management through addition of N-fertilizers. Conclusions: Our study showed that management practices applied to agricultural soils might not significantly affect the bacterial and fungal communities, but cause slow and long-term changes in the abundance and structure of the archaeal community. Moreover, the results suggest that, to different extents, abiotic and biotic factors determine the community assembly of archaeal, bacterial and fungal communities.

A Simple Artificial Life Model Explains Irrational Behavior in Human Decision-Making

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats' neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments.

Using Prior Information from the Medical Literature in GWAS of Oral Cancer Identifies Novel Susceptibility Variant on Chromosome 4-the AdAPT Method

Background: Genome-wide association studies (GWAS) require large sample sizes to obtain adequate statistical power, but it may be possible to increase the power by incorporating complementary data. In this study we investigated the feasibility of automatically retrieving information from the medical literature and leveraging this information in GWAS. Methods: We developed a method that searches through PubMed abstracts for pre-assigned keywords and key concepts, and uses this information to assign prior probabilities of association for each single nucleotide polymorphism (SNP) with the phenotype of interest - the Adjusting Association Priors with Text (AdAPT) method. Association results from a GWAS can subsequently be ranked in the context of these priors using the Bayes False Discovery Probability (BFDP) framework. We initially tested AdAPT by comparing rankings of known susceptibility alleles in a previous lung cancer GWAS, and subsequently applied it in a two-phase GWAS of oral cancer. Results: Known lung cancer susceptibility SNPs were consistently ranked higher by AdAPT BFDPs than by p-values. In the oral cancer GWAS, we sought to replicate the top five SNPs as ranked by AdAPT BFDPs, of which rs991316, located in the ADH gene region of 4q23, displayed a statistically significant association with oral cancer risk in the replication phase (per-rare-allele log additive p-value [p(trend)] = 2.5 x 10(-3)). The combined OR for having one additional rare allele was 0.83 (95% CI: 0.76-0.90), and this association was independent of previously identified susceptibility SNPs that are associated with overall UADT cancer in this gene region. We also investigated if rs991316 was associated with other cancers of the upper aerodigestive tract (UADT), but no additional association signal was found. Conclusion: This study highlights the potential utility of systematically incorporating prior knowledge from the medical literature in genome-wide analyses using the AdAPT methodology. AdAPT is available online (url: http://services.gate.ac.uk/lld/gwas/service/config).

Functional diversity of bacterial genes associated with aromatic hydrocarbon degradation in anthropogenic dark earth of Amazonia

The objective of this work was to evaluate the catabolic gene diversity for the bacterial degradation of aromatic hydrocarbons in anthropogenic dark earth of Amazonia (ADE) and their biochar (BC). Functional diversity analyses in ADE soils can provide information on how adaptive microorganisms may influence the fertility of soils and what is their involvement in biogeochemical cycles. For this, clone libraries containing the gene encoding for the alpha subunit of aromatic ring-hydroxylating dioxygenases (alpha-A RH D bacterial gene) were constructed, totaling 800 clones. These libraries were prepared from samples of an ADE soil under two different land uses, located at the Caldeirao Experimental Station secondary forest (SF) and agriculture (AG)-, and the biochar (SF_BC and AG_BC, respectively). Heterogeneity estimates indicated greater diversity in BC libraries; and Venn diagrams showed more unique operational protein clusters (OPC) in the SF_BC library than the ADE soil, which indicates that specific metabolic processes may occur in biochar. Phylogenetic analysis showed unidentified dioxygenases in ADE soils. Libraries containing functional gene encoding for the alpha subunit of the aromatic ring-hydroxylating dioxygenases (ARHD) gene from biochar show higher diversity indices than those of ADE under secondary forest and agriculture.